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Statistical methods for resolving poor uncertainty quantification in machine learning interatomic potentials
Emil Annevelink, Venkatasubramanian Viswanathan
Machine learning interatomic potentials (MLIPs) are promising surrogates for quantum mechanics evaluations in ab-initio molecular dynamics simulations due to their ability to repro…
Generalizable data-driven turbulence closure modeling on unstructured grids with differentiable physics
Hojin Kim, Varun Shankar, Venkatasubramanian Viswanathan +1
Differentiable physical simulators are proving to be valuable tools for developing data-driven models for computational fluid dynamics (CFD). In particular, these simulators enable…
Importance of equivariant and invariant symmetries for fluid flow modeling
Varun Shankar, Shivam Barwey, Zico Kolter +2
Graph neural networks (GNNs) have shown promise in learning unstructured mesh-based simulations of physical systems, including fluid dynamics. In tandem, geometric deep learning pr…
Differentiable Turbulence: Closure as a partial differential equation constrained optimization
Varun Shankar, Dibyajyoti Chakraborty, Venkatasubramanian Viswanathan +1
Deep learning is increasingly becoming a promising pathway to improving the accuracy of sub-grid scale (SGS) turbulence closure models for large eddy simulations (LES). We leverage…
Twisto-electrochemical activity volcanoes in Trilayer Graphene
Mohammad Babar, Ziyan Zhu, Rachel Kurchin +2
In this work, we develop a twist-dependent electrochemical activity map, combining a tight-binding electronic structure model with modified Marcus-Hush-Chidsey kinetics in trilayer…
Accurate Surface and Finite Temperature Bulk Properties of Lithium Metal at Large Scales using Machine Learning Interaction Potentials
Mgcini Keith Phuthi, Archie Mingze Yao, Simon Batzner +4
The properties of lithium metal are key parameters in the design of lithium ion and lithium metal batteries. They are difficult to probe experimentally due to the high reactivity a…